Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the ...

The problem of missing data is often considered to be the most important obstacle in reconstructing the phylogeny of fossil taxa and in combining data from diverse characters and taxa for phylogenetic analysis. Empirical and theoretical studies show that including highly incomplete taxa can lead to multiple equally parsimonious trees, poorly resolved consensus trees, and decreased phylogenetic ...

Thematic maps have a central and often unquestioned role in geographical information systems (GIS) (Woodcock and Gopal, 2000). They are one of the most common ways of representing spatial data and are unsurprisingly, therefore, a common input and output of analyses undertaken in GIS. They are, however, only a model or simplification and hence a flawed representation of reality. Consequently, it...

Typical wireless sensor networks (WSNs) applications are characterized by a certain number of different requirements such as: data accuracy, localization, reputation, security, and confidentiality. Moreover, being often battery powered, WSNs face the challenge of ensuring privacy and security despite power consumption limitations. When the application scenario allows their use, data aggregation...

Predictive data mining typically relies on labeled data without exploiting a much larger amount of available unlabeled data. The goal of this paper is to show that using unlabeled data can be beneficial in a range of important prediction problems and therefore should be an integral part of the learning process. Given an unlabeled dataset representative of the underlying distribution and a K-cla...